At the same time, DNNs themselves are getting better and better on more human-like tasks. Recently, [1]} proposed a model that could simultaneously learn visual and linguistic information from a huge dataset using a constrastive loss function. Importantly, this multimodal model, known as CLIP, was found to possess neurons in its last layer that encoded specific concepts [2]}. These artificial neurons are reminiscent of `concept cells’ in the human medial temporal lobe (MTL)  [3]}, [4]}, biological neurons that appear to represent the meaning of a given stimulus or concept in a manner that is invariant to how that stimulus is actually experienced by the observer. For example, a single neuron in the human hippocampus showed incredible specificity in its response to the actress Halle Berry. This neuron responded to different images of the actress, including to photographs in which she was disguised as Catwoman (her starring role in a movie by the same name). The same neuron also responded to a semantic representation of the concept, i.e. to the letter string “HALLE BERRY”. Other studies have since shown that “concept cells'’ are also activated when stimulus information is provided in other sensory modalities, for example when the name of the person is spoken out loud [5]}.
